Development of CMP pad using an unpatterned surface inspection system
Bibliographic record
Abstract
As the device continues to shrink, copper(Cu) chemical mechanical planarization (CMP) remains a challenging process for copper dual damascene processes. In the past, the standard blanket wafer defectivity detection system for CMP processes focused on particle or scratch count and characterization. However the standard methodology is no longer suitable for detecting anomalous process defects while technology nodes continue to scale down. Accessing defectivity information below standard thresholds can be achieved by wafer haze analysis. Wafer haze information can be used in addition to standard defectivity data to optimize CMP processes and characterize CMP defects. Haze analysis represents a powerful tool for capturing spatial signatures caused by CMP processes. [1, 2] This study shows how we applied KLA-Tencor's SURFmonitor with grid analysis to reveal defect signatures not apparent in the traditional darkfield defect map. This new method of using haze analysis to measure haze defects on Cu blanket wafers was used for characterization and development of CMP pads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".